Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks
Abstract As the number of service requests for applications continues increasing due to various conditions, the limitations on the number of resources provide a barrier in providing the applications with the appropriate Quality of Service (QoS) assurances. As a result, an efficient scheduling mechan...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93365-y |
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| author | R. Augustian Isaac P. Sundaravadivel V. S. Nici Marx G. Priyanga |
| author_facet | R. Augustian Isaac P. Sundaravadivel V. S. Nici Marx G. Priyanga |
| author_sort | R. Augustian Isaac |
| collection | DOAJ |
| description | Abstract As the number of service requests for applications continues increasing due to various conditions, the limitations on the number of resources provide a barrier in providing the applications with the appropriate Quality of Service (QoS) assurances. As a result, an efficient scheduling mechanism is required to determine the order of handling application requests, as well as the appropriate use of a broadcast media and data transfer. In this paper an innovative approach, incorporating the Crossover and Mutation (CM)-centered Marine Predator Algorithm (MPA) is introduced for an effective resource allocation. This strategic resource allocation optimally schedules resources within the Vehicular Edge computing (VEC) network, ensuring the most efficient utilization. The proposed method begins by the meticulous feature extraction from the Vehicular network model, with attributes such as mobility patterns, transmission medium, bandwidth, storage capacity, and packet delivery ratio. For further analysis the Elephant Herding Lion Optimizer (EHLO) algorithm is employed to pinpoint the most critical attributes. Subsequently the Modified Fuzzy C-Means (MFCM) algorithm is used for efficient vehicle clustering centred on selected attributes. These clustered vehicle characteristics are then transferred and stored within the cloud server infrastructure. The performance of the proposed methodology is evaluated using MATLAB software using simulation method. This study offers a comprehensive solution to the resource allocation challenge in Vehicular Cloud Networks, addresses the burgeoning demands of modern applications while ensuring QoS assurances and signifies a significant advancement in the field of VEC. |
| format | Article |
| id | doaj-art-9de9aae145f64168b2e7158df2b706a2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9de9aae145f64168b2e7158df2b706a22025-08-20T03:41:40ZengNature PortfolioScientific Reports2045-23222025-03-0115111810.1038/s41598-025-93365-yEnhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networksR. Augustian Isaac0P. Sundaravadivel1V. S. Nici Marx2G. Priyanga3Saveetha Engineering CollegeSaveetha Engineering CollegeSaveetha Engineering CollegeSaveetha Engineering CollegeAbstract As the number of service requests for applications continues increasing due to various conditions, the limitations on the number of resources provide a barrier in providing the applications with the appropriate Quality of Service (QoS) assurances. As a result, an efficient scheduling mechanism is required to determine the order of handling application requests, as well as the appropriate use of a broadcast media and data transfer. In this paper an innovative approach, incorporating the Crossover and Mutation (CM)-centered Marine Predator Algorithm (MPA) is introduced for an effective resource allocation. This strategic resource allocation optimally schedules resources within the Vehicular Edge computing (VEC) network, ensuring the most efficient utilization. The proposed method begins by the meticulous feature extraction from the Vehicular network model, with attributes such as mobility patterns, transmission medium, bandwidth, storage capacity, and packet delivery ratio. For further analysis the Elephant Herding Lion Optimizer (EHLO) algorithm is employed to pinpoint the most critical attributes. Subsequently the Modified Fuzzy C-Means (MFCM) algorithm is used for efficient vehicle clustering centred on selected attributes. These clustered vehicle characteristics are then transferred and stored within the cloud server infrastructure. The performance of the proposed methodology is evaluated using MATLAB software using simulation method. This study offers a comprehensive solution to the resource allocation challenge in Vehicular Cloud Networks, addresses the burgeoning demands of modern applications while ensuring QoS assurances and signifies a significant advancement in the field of VEC.https://doi.org/10.1038/s41598-025-93365-yVehicular edge computing (VEC)Crossover and mutation (CM)Marine predator algorithm (MPA)Elephant herding Lion optimizer (EHLO)Modified fuzzy c-means (MFCM) algorithmCloud server |
| spellingShingle | R. Augustian Isaac P. Sundaravadivel V. S. Nici Marx G. Priyanga Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks Scientific Reports Vehicular edge computing (VEC) Crossover and mutation (CM) Marine predator algorithm (MPA) Elephant herding Lion optimizer (EHLO) Modified fuzzy c-means (MFCM) algorithm Cloud server |
| title | Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks |
| title_full | Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks |
| title_fullStr | Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks |
| title_full_unstemmed | Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks |
| title_short | Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks |
| title_sort | enhanced novelty approaches for resource allocation model for multi cloud environment in vehicular ad hoc networks |
| topic | Vehicular edge computing (VEC) Crossover and mutation (CM) Marine predator algorithm (MPA) Elephant herding Lion optimizer (EHLO) Modified fuzzy c-means (MFCM) algorithm Cloud server |
| url | https://doi.org/10.1038/s41598-025-93365-y |
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